Predictive models or algorithms are employed to predict the likelihood of an outcome. One application of predictive models is in risk assessment. Most enterprises in e-commerce and financial industries use some form of predictive models to detect fraud and intrusion in their transaction system. For example, fraud can include conducting unauthorized business on someone else's payment instrument. A decision scientist or statistician is conventionally employed to build a predictive model to identify high-risk behavior. Model performance is periodically monitored, and the model is re-developed when the performance level falls below a predetermined level. Further, sophisticated models are often designed to improve classification based on an abundance of historical data. For instance, a model can be trained to classify behavior has high or low risk as a function of historical data for a particular business.